This paper, an addendum to “Dialectical Thermodynamics’ solution to the conceptual imbroglio that is the reversible path”, this journal, 10, 775-799, was written in response to the requests of several readers to pr...This paper, an addendum to “Dialectical Thermodynamics’ solution to the conceptual imbroglio that is the reversible path”, this journal, 10, 775-799, was written in response to the requests of several readers to provide further evidence of the said “imbroglio”. The evidence here presented relates to the incompatibility existing between the total-entropy and the Gibbs energy prescriptions for the reversible path. The previously published proof of the negentropic nature of the transformation of heat into work is here included to validate out conclusions about the Gibbs energy perspective.展开更多
Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narr...Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT^(*)-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT^(*),the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT^(*).Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications.展开更多
Discussions on Chinese modernization are offering African countries both conceptual inspiration and practical references as they explore their own sustainable development paths.
Q-learning is a classical reinforcement learning method with broad applicability.It can respond effectively to environmental changes and provide flexible strategies,making it suitable for solving robot path-planning p...Q-learning is a classical reinforcement learning method with broad applicability.It can respond effectively to environmental changes and provide flexible strategies,making it suitable for solving robot path-planning problems.However,Q-learning faces challenges in search and update efficiency.To address these issues,we propose an improved Q-learning(IQL)algorithm.We use an enhanced Ant Colony Optimization(ACO)algorithmto optimizeQtable initialization.We also introduce the UCH mechanism to refine the reward function and overcome the exploration dilemma.The IQL algorithm is extensively tested in three grid environments of different scales.The results validate the accuracy of themethod and demonstrate superior path-planning performance compared to traditional approaches.The algorithm reduces the number of trials required for convergence,improves learning efficiency,and enables faster adaptation to environmental changes.It also enhances stability and accuracy by reducing the standard deviation of trials to zero.On grid maps of different sizes,IQL achieves higher expected returns.Compared with the original Q-learning algorithm,IQL improves performance by 12.95%,18.28%,and 7.98% on 10*10,20*20,and 30*30 maps,respectively.The proposed algorithm has promising applications in robotics,path planning,intelligent transportation,aerospace,and game development.展开更多
Topological phases are governed by lattice symmetries,yet how different symmetry-breaking paths(SBPs)affect topological transitions remains insufficiently understood.Most existing studies rely on a single SBP,and addr...Topological phases are governed by lattice symmetries,yet how different symmetry-breaking paths(SBPs)affect topological transitions remains insufficiently understood.Most existing studies rely on a single SBP,and address only one bandgap,limiting independent control of multiple gaps.Here,we investigate multiple isolated Dirac points in a trefoil-knot-modified honeycomb lattice,and show that a single SBP generally inverts all relevant Dirac points simultaneously,whereas the tailored combinations of SBPs enable selective and programmable band inversion at targeted gaps.The excitation-dependent responses reveal strong modal selectivity.This capability is exploited to realize independently controllable multi-channel signal splitting,which is unattainable with a single SBP.The results enable SBPs as an effective design degree of freedom for programmable and reconfigurable topological elastic devices.展开更多
Dimethylphenols serve as important intermediates in synthesizing pharmaceuticals and agrochemicals,yet traditional distillation struggles to separate their isomers due to minimal boiling point differences,and the deve...Dimethylphenols serve as important intermediates in synthesizing pharmaceuticals and agrochemicals,yet traditional distillation struggles to separate their isomers due to minimal boiling point differences,and the development of melt crystallization is hampered by lacking solid–liquid equilibrium (SLE) data for some isomers.Therefore,the SLE data of both binary and ternary mixtures of 2,3-dimethylphenol (2,3-DMP),3,5-dimethylphenol (3,5-DMP),and 3,4-dimethylphenol (3,4-DMP) were determined by using differential scanning calorimetry in this work.Additionally,crystallographic analysis was conducted to investigate the thermodynamic characteristics of these mixtures.The experimental results indicated that all the systems investigated in this research exhibited eutectic behavior.The experimentally obtained SLE data were well correlated with the Wilson and non-random two-liquid models.The excess thermodynamic functions were calculated to analyze the types and intensities of the molecular interactions occurring in the mixtures.Furthermore,this study developed a model for the correlation between the theoretical crystallization yield and the actual cooling yield and final yield in melt crystallization.This study has furnished reliable data essential for developing and optimizing the melt crystallization process of mixtures of 2,3-DMP,3,5-DMP,and 3,4-DMP.展开更多
Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees l...Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees lead to slow convergence and force an unfavorable trade-off between path quality and traversal safety.To address these challenges,we introduce HS-APF-RRT*,a novel algorithm that fuses layered sampling,an enhanced Artificial Potential Field(APF),and a dynamic neighborhood-expansion mechanism.First,the workspace is hierarchically partitioned into macro,meso,and micro sampling layers,progressively biasing random samples toward safer,lower-energy regions.Second,we augment the traditional APF by incorporating a slope-dependent repulsive term,enabling stronger avoidance of steep obstacles.Third,a dynamic expansion strategy adaptively switches between 8 and 16 connected neighborhoods based on local obstacle density,striking an effective balance between search efficiency and collision-avoidance precision.In simulated off-road scenarios,HS-APF-RRT*is benchmarked against RRT*,GoalBiased RRT*,and APF-RRT*,and demonstrates significantly faster convergence,lower path-energy consumption,and enhanced safety margins.展开更多
Excavation causes stress redistribution and affects the stress path during the shearing process of rock.The shear strength of rock varies under different stress paths,and the presence of defects reduces the shear stre...Excavation causes stress redistribution and affects the stress path during the shearing process of rock.The shear strength of rock varies under different stress paths,and the presence of defects reduces the shear strength.To further investigate this phenomenon,this study investigates the shear behaviour of rocks with different shear surface integrities under the influenceof different stress paths through laboratory tests and numerical simulations.The results indicate that the shear strength depends on the stress path and a decrease in the shear surface integrity reduces the degree of dependence.The cohesion and friction angle of the Mohr‒Coulomb criterion decrease with weakening of the shear surface integrity.For different stress paths,the direct shear strength is always greater than that of other shear stress paths.The pattern of changes in the acoustic emission count and cumulative count indirectly reflectsthe above findings.Numerical simulations further indicate that the different principal stress states and normal suppression effects during the shearing process lead to changes in the factors of crack propagation,resulting in different mechanical behaviours under various stress paths.For rocks with different integrity levels,the main reason for the different path dependences of shear strength is that the size of the area affected by shear is different.Shear failure will concentrate on the shear plane when the normal inhibition effect is greater.This study explores the mechanism of rock shear behaviour,providing a theoretical basis for establishing more accurate constitutive models and strength criteria.展开更多
To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense,dynamic,unpredictable obstacles challenging conventional methods—this p...To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense,dynamic,unpredictable obstacles challenging conventional methods—this paper proposes a hybrid algorithm integrating Q-learning and improved A*-Artificial Potential Field(A-APF).Centered on theQ-learning framework,the algorithmleverages safety-oriented guidance generated byA-APF and employs a dynamic coordination mechanism that adaptively balances exploration and exploitation.The proposed system comprises four core modules:(1)an environment modeling module that constructs grid-based obstacle maps;(2)an A-APF module that combines heuristic search from A*algorithm with repulsive force strategies from APF to generate guidance;(3)a Q-learning module that learns optimal state-action values(Q-values)through spraying robot-environment interaction and a reward function emphasizing path optimality and safety;and(4)a dynamic optimization module that ensures adaptive cooperation between Q-learning and A-APF through exploration rate control and environment-aware constraints.Simulation results demonstrate that the proposed method significantly enhances path safety in complex underground mining environments.Quantitative results indicate that,compared to the traditional Q-learning algorithm,the proposed method shortens training time by 42.95% and achieves a reduction in training failures from 78 to just 3.Compared to the static fusion algorithm,it further reduces both training time(by 10.78%)and training failures(by 50%),thereby improving overall training efficiency.展开更多
This paper develops a semi-analytical solution for pile penetration in natural soft clays using the strain path method(SPM).The stress-strain behavior of soils is characterized by the S-CLAY1S model,which can capture ...This paper develops a semi-analytical solution for pile penetration in natural soft clays using the strain path method(SPM).The stress-strain behavior of soils is characterized by the S-CLAY1S model,which can capture the anisotropic evolution and destructuring nature of soft clays.By integrating the S-CLAY1S model into the theoretical framework of the SPM,a set of ordinary differential equations is formulated with respect to the vertical coordinate of soil particles.The distribution of excess pore water pressure(EPWP)following pile installation is approximated through one-dimensional(1D)radial integration around the pile shaft.The distribution of stresses and EPWP,along with the evolution of fabric anisotropy within the soil surrounding the pile,is presented to illustrate the response of pile penetration in natural soft clays.The proposed solution is validated against existing theoretical solutions using the SPM and cavity expansion method(CEM),along with experimental data.The findings demonstrate that the SPM reveals lower radial effective stresses and EPWP at the pile shaft than that of CEM.Pile penetration alters the soil's anisotropic properties,inducing rotational hardening and affecting post-installation stress distribution.Soil destructuration eliminates bonding among particles near the pile,resulting in a complete disruption of soil structure at the pile surface,which is particularly pronounced for higher initial soil structure ratios.Minimal variation was observed in the three principal stresses and shear stress on the cone side surface as the angle increased from 18°to 60°,except for a slight reduction in EPWP.展开更多
Separation of 2-methylfuran(2-MF)and methanol(MeOH)azeotropes is a key challenge in biofuel production because of the efficiency and sustainability issues of conventional methods.In this study,ionic liquids(ILs)were i...Separation of 2-methylfuran(2-MF)and methanol(MeOH)azeotropes is a key challenge in biofuel production because of the efficiency and sustainability issues of conventional methods.In this study,ionic liquids(ILs)were introduced as green solvents for separation of 2-MF/MeOH through liquid–liquid equilibrium(LLE)experiment.Three ILs,namely 1-ethyl-3-methylimidazole dihydrogen phosphate([EMIM][H_(2)PO_(4)]),1-propyl-3-methylimidazole dihydrogen phosphate([PMIM][H_(2)PO_(4)])and 1-butyl-3-methylimidazole dihydrogen phosphate([BMIM][H_(2)PO_(4)]),were screened out from 425 candidates using the conductor-like screening model for real solvents(COSMO-RS).Then,the ternary LLE data of 2-MF(1)+MeOH(2)+ILs(3)were determined at 30℃ and 101.32 kPa.Results confirmed[EMIM][H_(2)PO_(4)]as the best performer,achieving a selectivity of 343.86 and a distribution coefficient of 36.66 for MeOH—significantly higher than[PMIM][H_(2)PO_(4)]and[BMIM][H_(2)PO_(4)].The accuracy of the LLE data was verified by Othmer–Tobias and Hand equations(R^(2)>0.90).The non-random two liquid model was used to correlate the experimental data(RMSD<2%).Besides,the combination of electrostatic surfaces potential,independent gradient model based on Hirshfeld partition,mean square displacement and radial distribution functions revealed strong electrostatic interactions between[H_(2)PO_(4)]^(–) and MeOH.Interaction energy analysis further emphasizes the mechanism of MeOH separation from a mixture of 2-MF and MeOH by ILs.This work provides a multiscale strategy for the separation of 2-MF and MeOH azeotropes,highlighting the potential of ILs to improve biofuel purification while reducing energy and environmental costs.展开更多
The equilibrium dynamics and nonlinear rheology of unentangled polymer blends remain inadequately understood,especially regarding the influence of short-chain matrix length N_(S) on the structure and rheological behav...The equilibrium dynamics and nonlinear rheology of unentangled polymer blends remain inadequately understood,especially regarding the influence of short-chain matrix length N_(S) on the structure and rheological behavior of dispersed long chains.Using molecular dynamics simulations based on the Kremer-Grest model,we systematically explore the N_(S)-dependence of static conformations,equilibrium dynamics,and nonlinear shear responses in unentangled long-chain/short-chain polymer blends.Our results demonstrate a decoupling between the static and dynamic sensitivity to N_(S):while the static chain size,R_g,follows Flory theory with slight swelling at small N_(S) due to incomplete excluded volume screening,the diffusion coefficient,D,and the relaxation time,τ_(0),exhibit a strong,non-monotonic N_(S)-dependence,transitioning from monomeric friction dominance at small N_(S) to collective segmental rearrangement at large N_(S).Additionally,we observe partial decoupling between the viscous and normal stress responses:while the zero-shear viscosity,η,is strongly N_(S)-dependent,the first and second normal stress coefficients,Ψ_(1) and Ψ_(2),collapse onto universal curves when scaled by the dimensionless shear rate,γτ_(0),suggesting a common mechanism of orientation and stretching.Under shear,long chains compress in the vorticity direction λ_(z)~Wi^(-0.2),which reduces collision frequency and contributes to shear thinning,while the scaling of weaker orientation resistance m_(G)~Wi^(0.35)reflects hydrodynamic screening by the short-chain matrix.These findings highlight the limitations of single-chain models and emphasize the necessity of considering N_(S)-dependent matrix dynamics and flow-induced structural changes in understanding the rheology of unentangled polymer blends.展开更多
This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japo...This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japonicus.The math-ematical model is developed by simulating various biological behaviors of the Gekko japonicus,such as hybrid loco-motion patterns,directional olfactory guidance,implicit group advantage tendencies,and the tail autotomy mechanism.By integrating multi-stage mutual constraints and dynamically adjusting parameters,GJA maintains an optimal balance between global exploration and local exploitation,thereby effectively solving complex optimization problems.To assess the performance of GJA,comparative analyses were performed against fourteen state-of-the-art metaheuristic algorithms using the CEC2017 and CEC2022 benchmark test sets.Additionally,a Friedman test was performed on the experimen-tal results to assess the statistical significance of differences between various algorithms.And GJA was evaluated using multiple qualitative indicators,further confirming its superiority in exploration and exploitation.Finally,GJA was utilized to solve four engineering optimization problems and further implemented in robotic path planning to verify its practical applicability.Experimental results indicate that,compared to other high-performance algorithms,GJA demonstrates excep-tional performance as a powerful optimization algorithm in complex optimization problems.We make the code publicly available at:https://github.com/zhy1109/Gekko-japonicusalgorithm.展开更多
When a porous rock is subjected to overall compressive loading,either increasing pore pressure or decreasing confining pressure could result in rock failure.The stress path and the applied pressure change rate may aff...When a porous rock is subjected to overall compressive loading,either increasing pore pressure or decreasing confining pressure could result in rock failure.The stress path and the applied pressure change rate may affect the initiation and propagation of fractures within brittle materials.Understanding the physical mechanisms leading to failure is crucial for underground engineering applications and geo-energy exploration and storage.We conducted triaxial compression experiments on porous Bentheim sandstone samples at different stress paths and pressure change rates.First,at a constant confining pressure of 35 MPa and pore pressure of 5 MPa,intact cylindrical samples were axially loaded up to about 85%of the peak strength.Subsequently,the axial piston position was fixed,and then either the pore pressure was increased or the confining pressure was decreased at two different rates(0.5 MPa/min or 2 MPa/min),leading to final catastrophic failure.The mechanical results revealed that samples subjected to higher rates of decreasing effective confining pressure exhibited larger stress drop rates,higher slip rates,higher total breakdown work,higher rates of acoustic emissions(AEs)before failure,and higher post-failure AE decay rates.In contrast,the applied stress path did not significantly affect rock failure characteristics.Comparison of located AE events with post-mortem microstructures of deformed samples shows a good agreement.The AE source type determined from the P-wave first-motion polarity shows that shear failure dominated the fracture process when approaching failure.Gutenberg-Richter b-values revealed a significant decrease before failure in all tests.Our results indicate that,in contrast to the stress path,the rate of effective stress change strongly affects fracturing behavior and AE rate changes.展开更多
With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper pro...With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper proposes an intelligent path planning framework based on grid map modeling.First,an improved Safe and Smooth A*(SSA*)algorithm is employed for global path planning.By incorporating obstacle expansion and cornerpoint optimization,the proposed SSA*enhances the safety and smoothness of the planned path.Then,a Partitioned Dynamic Window Approach(PDWA)is integrated for local planning,which is triggered when dynamic or sudden static obstacles appear,enabling real-time obstacle avoidance and path adjustment.A unified objective function is constructed,considering path length,safety,and smoothness comprehensively.Multiple simulation experiments are conducted on typical port grid maps.The results demonstrate that the improved SSA*significantly reduces the number of expanded nodes and computation time in static environmentswhile generating smoother and safer paths.Meanwhile,the PDWA exhibits strong real-time performance and robustness in dynamic scenarios,achieving shorter paths and lower planning times compared to other graph search algorithms.The proposedmethodmaintains stable performance across maps of different scales and various port scenarios,verifying its practicality and potential for wider application.展开更多
Guided by the significant theoretical principle of the“Two Integrations”and grounded in Marxist cultural theory as its methodological basis,this paper constructs a bidirectional interpretative model linking“Yellow ...Guided by the significant theoretical principle of the“Two Integrations”and grounded in Marxist cultural theory as its methodological basis,this paper constructs a bidirectional interpretative model linking“Yellow River Culture”with“Cultural Confidence”.It proposes an integrated“Objective-Content-Path-Support”framework.Through the synergy of three-dimensional objectives,adaptation of stratified content,innovation in four-dimensional pathways,and support from a three-dimensional guarantee system,this framework establishes a closed-loop operational mechanism of“Curriculum-Practice-Evaluation-Feedback”.The study focuses on core issues in integrating Yellow River culture into university education practices,such as content construction,methodological pathways,and institutional guarantees.It aims to provide a systematic reference for universities to fulfill their fundamental task of“fostering virtue and cultivating talent”and to serve the national strategies for ecological protection and high-quality development in the Yellow River Basin.展开更多
Taking the rural low-income population of Zhejiang Province as its subject, this paper examines how to build a sustainable income-growth mechanism and identify feasible implementation paths within the context of the c...Taking the rural low-income population of Zhejiang Province as its subject, this paper examines how to build a sustainable income-growth mechanism and identify feasible implementation paths within the context of the common prosperity strategy. The research identifies key obstacles to income expansion, including an undiversified industrial structure, insufficient human capital, and a lack of robust social protection. These call for systemic solutions featuring institutional innovation, resource consolidation, and capability enhancement. Building on Zhejiang's experience as a common prosperity demonstration zone, the article constructs an integrated framework centered on four pillars: industrial empowerment, education upgrading, social security reinforcement, and digital coordination. It further offers concrete policy proposals involving the cultivation of localized industries, vocational skill training, enhanced safety nets, and the adoption of digital tools. The study thus offers both theoretical insights and practical paradigms for tackling the challenge of raising incomes in low-income rural areas.展开更多
Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always ...Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always influenced by external factors,making the original path planning solution ineffective.In this paper,the multi-depot multi-UAV path planning problem with uncertain flight time is modeled as a robust optimization model with a budget uncertainty set.Then,the robust optimization model is transformed into a mixed integer linear programming model by the strong duality theorem,which makes the problem easy to solve.To effectively solve large-scale instances,a simulated annealing algorithm with a robust feasibility check(SA-RFC)is developed.The numerical experiment shows that the SA-RFC can find high-quality solutions within a few seconds.Moreover,the effect of the task location distribution,depot counts,and variations in robustness parameters on the robust optimization solution is analyzed by using Monte Carlo experiments.The results demonstrate that the proposed robust model can effectively reduce the risk of the UAV failing to return to the depot without significantly compromising the profit.展开更多
Shenzhen,a major city in southern China,has experienced rapid advancements in Unmanned Aerial Vehicle(UAV)technology,resulting in extensive logistics networks with thousands of daily flights.However,frequent disruptio...Shenzhen,a major city in southern China,has experienced rapid advancements in Unmanned Aerial Vehicle(UAV)technology,resulting in extensive logistics networks with thousands of daily flights.However,frequent disruptions due to its subtropical monsoon climate,including typhoons and gusty winds,present ongoing challenges.Despite the growing focus on operational costs and third-party risks,research on low-altitude urban wind fields remains scarce.This study addresses this gap by integrating wind field analysis into UAV path planning,introducing key innovations to the classical model.First,UAV wind resistance and turbulence constraints are analyzed,mapping high-wind-speed and turbulence-prone zones in the airspace.Second,wind dynamics are incorporated into path planning by considering airspeed and groundspeed variation,optimizing waypoint selection and flight speed adjustments to improve overall energy efficiency.Additionally,a wind-aware Theta*algorithm is proposed,leveraging wind vectors to expedite search process,while Computational Fluid Dynamics(CFD)techniques are employed to calculate wind fields.A case study of Shenzhen,examining wind patterns over the past decade,demonstrates a 6.23%improvement in groundspeed and a 7.69%reduction in energy consumption compared to wind-agnostic models.This framework advances UAV logistics by enhancing route safety and energy efficiency,contributing to more cost-effective operations.展开更多
Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This stu...Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller(NFIDC)with a Feedback Radial Basis Function Neural Network(FRBFNN).The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1.The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.A two-stage simulation evaluation was conducted.In the first stage,the controller was tested in a simulated hospital environment under both ideal and non-ideal conditions.In the second,it was benchmarked against four established controllers-Neural Network Model Reference Adaptive(NNMRA),Z-number Fuzzy Logic(Z-FL),Adaptive Dynamic Controller(ADC),and Fuzzy Logic-PID(FL-PID)—using circular and lemniscate trajectories.Across ten runs,the proposed controller achieved the lowest tracking errors under all conditions.Under ideal conditions,it achieved average improvements of 55.24%,75.75%,and 55.20%in integral absolute error(IAE),integral squared error(ISE),and mean absolute error(MAE),respectively,with coefficient of variation(CV)reductions above 55%.Under non-ideal conditions,average improvements exceeded 64%in IAE,77%in ISE,and 66%in MAE,while maintaining CV reductions above 57%.These results confirm that the NFIDC-FRBFNN controller offers superior accuracy,robustness,and consistency for real-time path tracking in healthcare robotics.展开更多
文摘This paper, an addendum to “Dialectical Thermodynamics’ solution to the conceptual imbroglio that is the reversible path”, this journal, 10, 775-799, was written in response to the requests of several readers to provide further evidence of the said “imbroglio”. The evidence here presented relates to the incompatibility existing between the total-entropy and the Gibbs energy prescriptions for the reversible path. The previously published proof of the negentropic nature of the transformation of heat into work is here included to validate out conclusions about the Gibbs energy perspective.
基金National Natural Science Foundation of China(32301712)Natural Science Foundation of Jiangsu Province(BK20230548+3 种基金BK20250876)Project of Faculty of Agricultural Equipment of Jiangsu University(NGXB20240203)A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD-2023-87)Open Funding Project of the Key Laboratory of Modern Agricultural Equipment and Technology(Jiangsu University),Ministry of Education(MAET202101)。
文摘Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT^(*)-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT^(*),the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT^(*).Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications.
文摘Discussions on Chinese modernization are offering African countries both conceptual inspiration and practical references as they explore their own sustainable development paths.
基金Financial supports from the National Natural Science Foundation of China(GrantNo.52374123&51974144)Project of Liaoning Provincial Department of Education(GrantNo.LJKZ0340)Liaoning Revitalization Talents Program(Grant No.XLYC2211085)are greatly acknowledged.
文摘Q-learning is a classical reinforcement learning method with broad applicability.It can respond effectively to environmental changes and provide flexible strategies,making it suitable for solving robot path-planning problems.However,Q-learning faces challenges in search and update efficiency.To address these issues,we propose an improved Q-learning(IQL)algorithm.We use an enhanced Ant Colony Optimization(ACO)algorithmto optimizeQtable initialization.We also introduce the UCH mechanism to refine the reward function and overcome the exploration dilemma.The IQL algorithm is extensively tested in three grid environments of different scales.The results validate the accuracy of themethod and demonstrate superior path-planning performance compared to traditional approaches.The algorithm reduces the number of trials required for convergence,improves learning efficiency,and enables faster adaptation to environmental changes.It also enhances stability and accuracy by reducing the standard deviation of trials to zero.On grid maps of different sizes,IQL achieves higher expected returns.Compared with the original Q-learning algorithm,IQL improves performance by 12.95%,18.28%,and 7.98% on 10*10,20*20,and 30*30 maps,respectively.The proposed algorithm has promising applications in robotics,path planning,intelligent transportation,aerospace,and game development.
基金Project supported by the National Natural Science Foundation of China(Nos.12232015 and12572106)the National Key R&D Program of China(Nos.2024YFB3408700,2024YFB3408701,2024YFB3408703)the Natural Science Foundation of Shaanxi Province of China(No.2023-JC-YB-073)。
文摘Topological phases are governed by lattice symmetries,yet how different symmetry-breaking paths(SBPs)affect topological transitions remains insufficiently understood.Most existing studies rely on a single SBP,and address only one bandgap,limiting independent control of multiple gaps.Here,we investigate multiple isolated Dirac points in a trefoil-knot-modified honeycomb lattice,and show that a single SBP generally inverts all relevant Dirac points simultaneously,whereas the tailored combinations of SBPs enable selective and programmable band inversion at targeted gaps.The excitation-dependent responses reveal strong modal selectivity.This capability is exploited to realize independently controllable multi-channel signal splitting,which is unattainable with a single SBP.The results enable SBPs as an effective design degree of freedom for programmable and reconfigurable topological elastic devices.
基金funded by the National Natural Science Foundation of China(22308358,22208346,22421003)IPE Project for Frontier Basic Research(QYJC-2023-05)CAS Project for Young Scientists in Basic Research(YSBR-038).
文摘Dimethylphenols serve as important intermediates in synthesizing pharmaceuticals and agrochemicals,yet traditional distillation struggles to separate their isomers due to minimal boiling point differences,and the development of melt crystallization is hampered by lacking solid–liquid equilibrium (SLE) data for some isomers.Therefore,the SLE data of both binary and ternary mixtures of 2,3-dimethylphenol (2,3-DMP),3,5-dimethylphenol (3,5-DMP),and 3,4-dimethylphenol (3,4-DMP) were determined by using differential scanning calorimetry in this work.Additionally,crystallographic analysis was conducted to investigate the thermodynamic characteristics of these mixtures.The experimental results indicated that all the systems investigated in this research exhibited eutectic behavior.The experimentally obtained SLE data were well correlated with the Wilson and non-random two-liquid models.The excess thermodynamic functions were calculated to analyze the types and intensities of the molecular interactions occurring in the mixtures.Furthermore,this study developed a model for the correlation between the theoretical crystallization yield and the actual cooling yield and final yield in melt crystallization.This study has furnished reliable data essential for developing and optimizing the melt crystallization process of mixtures of 2,3-DMP,3,5-DMP,and 3,4-DMP.
基金supported in part by 14th Five Year National Key R&D Program Project(Project Number:2023YFB3211001)the National Natural Science Foundation of China(62273339,U24A201397).
文摘Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees lead to slow convergence and force an unfavorable trade-off between path quality and traversal safety.To address these challenges,we introduce HS-APF-RRT*,a novel algorithm that fuses layered sampling,an enhanced Artificial Potential Field(APF),and a dynamic neighborhood-expansion mechanism.First,the workspace is hierarchically partitioned into macro,meso,and micro sampling layers,progressively biasing random samples toward safer,lower-energy regions.Second,we augment the traditional APF by incorporating a slope-dependent repulsive term,enabling stronger avoidance of steep obstacles.Third,a dynamic expansion strategy adaptively switches between 8 and 16 connected neighborhoods based on local obstacle density,striking an effective balance between search efficiency and collision-avoidance precision.In simulated off-road scenarios,HS-APF-RRT*is benchmarked against RRT*,GoalBiased RRT*,and APF-RRT*,and demonstrates significantly faster convergence,lower path-energy consumption,and enhanced safety margins.
基金support from the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.KYCX24_2822)the Graduate Innovation Program of China University of Mining and Technology(Grant No.2024WLKXJ205)the National Natural Science Foundation of China(Grant No.52474157).
文摘Excavation causes stress redistribution and affects the stress path during the shearing process of rock.The shear strength of rock varies under different stress paths,and the presence of defects reduces the shear strength.To further investigate this phenomenon,this study investigates the shear behaviour of rocks with different shear surface integrities under the influenceof different stress paths through laboratory tests and numerical simulations.The results indicate that the shear strength depends on the stress path and a decrease in the shear surface integrity reduces the degree of dependence.The cohesion and friction angle of the Mohr‒Coulomb criterion decrease with weakening of the shear surface integrity.For different stress paths,the direct shear strength is always greater than that of other shear stress paths.The pattern of changes in the acoustic emission count and cumulative count indirectly reflectsthe above findings.Numerical simulations further indicate that the different principal stress states and normal suppression effects during the shearing process lead to changes in the factors of crack propagation,resulting in different mechanical behaviours under various stress paths.For rocks with different integrity levels,the main reason for the different path dependences of shear strength is that the size of the area affected by shear is different.Shear failure will concentrate on the shear plane when the normal inhibition effect is greater.This study explores the mechanism of rock shear behaviour,providing a theoretical basis for establishing more accurate constitutive models and strength criteria.
基金supported by the National Natural Science Foundation of China(Grant No.52374156).
文摘To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense,dynamic,unpredictable obstacles challenging conventional methods—this paper proposes a hybrid algorithm integrating Q-learning and improved A*-Artificial Potential Field(A-APF).Centered on theQ-learning framework,the algorithmleverages safety-oriented guidance generated byA-APF and employs a dynamic coordination mechanism that adaptively balances exploration and exploitation.The proposed system comprises four core modules:(1)an environment modeling module that constructs grid-based obstacle maps;(2)an A-APF module that combines heuristic search from A*algorithm with repulsive force strategies from APF to generate guidance;(3)a Q-learning module that learns optimal state-action values(Q-values)through spraying robot-environment interaction and a reward function emphasizing path optimality and safety;and(4)a dynamic optimization module that ensures adaptive cooperation between Q-learning and A-APF through exploration rate control and environment-aware constraints.Simulation results demonstrate that the proposed method significantly enhances path safety in complex underground mining environments.Quantitative results indicate that,compared to the traditional Q-learning algorithm,the proposed method shortens training time by 42.95% and achieves a reduction in training failures from 78 to just 3.Compared to the static fusion algorithm,it further reduces both training time(by 10.78%)and training failures(by 50%),thereby improving overall training efficiency.
基金support from the National Natural Science Foundation of China(Grant No.42407256)the State Key Laboratory of Hydraulics and Mountain River Engineering,China(Grant No.SKHL2113)the Sichuan Science and Technology Program(Grant No.2024YFHZ0341).
文摘This paper develops a semi-analytical solution for pile penetration in natural soft clays using the strain path method(SPM).The stress-strain behavior of soils is characterized by the S-CLAY1S model,which can capture the anisotropic evolution and destructuring nature of soft clays.By integrating the S-CLAY1S model into the theoretical framework of the SPM,a set of ordinary differential equations is formulated with respect to the vertical coordinate of soil particles.The distribution of excess pore water pressure(EPWP)following pile installation is approximated through one-dimensional(1D)radial integration around the pile shaft.The distribution of stresses and EPWP,along with the evolution of fabric anisotropy within the soil surrounding the pile,is presented to illustrate the response of pile penetration in natural soft clays.The proposed solution is validated against existing theoretical solutions using the SPM and cavity expansion method(CEM),along with experimental data.The findings demonstrate that the SPM reveals lower radial effective stresses and EPWP at the pile shaft than that of CEM.Pile penetration alters the soil's anisotropic properties,inducing rotational hardening and affecting post-installation stress distribution.Soil destructuration eliminates bonding among particles near the pile,resulting in a complete disruption of soil structure at the pile surface,which is particularly pronounced for higher initial soil structure ratios.Minimal variation was observed in the three principal stresses and shear stress on the cone side surface as the angle increased from 18°to 60°,except for a slight reduction in EPWP.
基金supported by the National Natural Science Foundation of China(22278272)Natural Science Foundation of Liaoning Province(2024-MS-129).
文摘Separation of 2-methylfuran(2-MF)and methanol(MeOH)azeotropes is a key challenge in biofuel production because of the efficiency and sustainability issues of conventional methods.In this study,ionic liquids(ILs)were introduced as green solvents for separation of 2-MF/MeOH through liquid–liquid equilibrium(LLE)experiment.Three ILs,namely 1-ethyl-3-methylimidazole dihydrogen phosphate([EMIM][H_(2)PO_(4)]),1-propyl-3-methylimidazole dihydrogen phosphate([PMIM][H_(2)PO_(4)])and 1-butyl-3-methylimidazole dihydrogen phosphate([BMIM][H_(2)PO_(4)]),were screened out from 425 candidates using the conductor-like screening model for real solvents(COSMO-RS).Then,the ternary LLE data of 2-MF(1)+MeOH(2)+ILs(3)were determined at 30℃ and 101.32 kPa.Results confirmed[EMIM][H_(2)PO_(4)]as the best performer,achieving a selectivity of 343.86 and a distribution coefficient of 36.66 for MeOH—significantly higher than[PMIM][H_(2)PO_(4)]and[BMIM][H_(2)PO_(4)].The accuracy of the LLE data was verified by Othmer–Tobias and Hand equations(R^(2)>0.90).The non-random two liquid model was used to correlate the experimental data(RMSD<2%).Besides,the combination of electrostatic surfaces potential,independent gradient model based on Hirshfeld partition,mean square displacement and radial distribution functions revealed strong electrostatic interactions between[H_(2)PO_(4)]^(–) and MeOH.Interaction energy analysis further emphasizes the mechanism of MeOH separation from a mixture of 2-MF and MeOH by ILs.This work provides a multiscale strategy for the separation of 2-MF and MeOH azeotropes,highlighting the potential of ILs to improve biofuel purification while reducing energy and environmental costs.
基金financially supported by the National Natural Science Foundation of China(Nos.22341304,22303100 and 12205270)the National Key R&D Program of China(Nos.2023YFA1008800 and 2020YFA0713601)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDC0180303)。
文摘The equilibrium dynamics and nonlinear rheology of unentangled polymer blends remain inadequately understood,especially regarding the influence of short-chain matrix length N_(S) on the structure and rheological behavior of dispersed long chains.Using molecular dynamics simulations based on the Kremer-Grest model,we systematically explore the N_(S)-dependence of static conformations,equilibrium dynamics,and nonlinear shear responses in unentangled long-chain/short-chain polymer blends.Our results demonstrate a decoupling between the static and dynamic sensitivity to N_(S):while the static chain size,R_g,follows Flory theory with slight swelling at small N_(S) due to incomplete excluded volume screening,the diffusion coefficient,D,and the relaxation time,τ_(0),exhibit a strong,non-monotonic N_(S)-dependence,transitioning from monomeric friction dominance at small N_(S) to collective segmental rearrangement at large N_(S).Additionally,we observe partial decoupling between the viscous and normal stress responses:while the zero-shear viscosity,η,is strongly N_(S)-dependent,the first and second normal stress coefficients,Ψ_(1) and Ψ_(2),collapse onto universal curves when scaled by the dimensionless shear rate,γτ_(0),suggesting a common mechanism of orientation and stretching.Under shear,long chains compress in the vorticity direction λ_(z)~Wi^(-0.2),which reduces collision frequency and contributes to shear thinning,while the scaling of weaker orientation resistance m_(G)~Wi^(0.35)reflects hydrodynamic screening by the short-chain matrix.These findings highlight the limitations of single-chain models and emphasize the necessity of considering N_(S)-dependent matrix dynamics and flow-induced structural changes in understanding the rheology of unentangled polymer blends.
基金CHINA POSTDOCTORAL SCIENCE FOUNDATION(Grant No.2025M771925)Young Scientists Fund(C Class)(Grant No.32501636)Special Fund of Fundamental Scientific Research Business Expense for Higher School of Central Government(Grant No.2572025JT04).
文摘This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japonicus.The math-ematical model is developed by simulating various biological behaviors of the Gekko japonicus,such as hybrid loco-motion patterns,directional olfactory guidance,implicit group advantage tendencies,and the tail autotomy mechanism.By integrating multi-stage mutual constraints and dynamically adjusting parameters,GJA maintains an optimal balance between global exploration and local exploitation,thereby effectively solving complex optimization problems.To assess the performance of GJA,comparative analyses were performed against fourteen state-of-the-art metaheuristic algorithms using the CEC2017 and CEC2022 benchmark test sets.Additionally,a Friedman test was performed on the experimen-tal results to assess the statistical significance of differences between various algorithms.And GJA was evaluated using multiple qualitative indicators,further confirming its superiority in exploration and exploitation.Finally,GJA was utilized to solve four engineering optimization problems and further implemented in robotic path planning to verify its practical applicability.Experimental results indicate that,compared to other high-performance algorithms,GJA demonstrates excep-tional performance as a powerful optimization algorithm in complex optimization problems.We make the code publicly available at:https://github.com/zhy1109/Gekko-japonicusalgorithm.
文摘When a porous rock is subjected to overall compressive loading,either increasing pore pressure or decreasing confining pressure could result in rock failure.The stress path and the applied pressure change rate may affect the initiation and propagation of fractures within brittle materials.Understanding the physical mechanisms leading to failure is crucial for underground engineering applications and geo-energy exploration and storage.We conducted triaxial compression experiments on porous Bentheim sandstone samples at different stress paths and pressure change rates.First,at a constant confining pressure of 35 MPa and pore pressure of 5 MPa,intact cylindrical samples were axially loaded up to about 85%of the peak strength.Subsequently,the axial piston position was fixed,and then either the pore pressure was increased or the confining pressure was decreased at two different rates(0.5 MPa/min or 2 MPa/min),leading to final catastrophic failure.The mechanical results revealed that samples subjected to higher rates of decreasing effective confining pressure exhibited larger stress drop rates,higher slip rates,higher total breakdown work,higher rates of acoustic emissions(AEs)before failure,and higher post-failure AE decay rates.In contrast,the applied stress path did not significantly affect rock failure characteristics.Comparison of located AE events with post-mortem microstructures of deformed samples shows a good agreement.The AE source type determined from the P-wave first-motion polarity shows that shear failure dominated the fracture process when approaching failure.Gutenberg-Richter b-values revealed a significant decrease before failure in all tests.Our results indicate that,in contrast to the stress path,the rate of effective stress change strongly affects fracturing behavior and AE rate changes.
文摘With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper proposes an intelligent path planning framework based on grid map modeling.First,an improved Safe and Smooth A*(SSA*)algorithm is employed for global path planning.By incorporating obstacle expansion and cornerpoint optimization,the proposed SSA*enhances the safety and smoothness of the planned path.Then,a Partitioned Dynamic Window Approach(PDWA)is integrated for local planning,which is triggered when dynamic or sudden static obstacles appear,enabling real-time obstacle avoidance and path adjustment.A unified objective function is constructed,considering path length,safety,and smoothness comprehensively.Multiple simulation experiments are conducted on typical port grid maps.The results demonstrate that the improved SSA*significantly reduces the number of expanded nodes and computation time in static environmentswhile generating smoother and safer paths.Meanwhile,the PDWA exhibits strong real-time performance and robustness in dynamic scenarios,achieving shorter paths and lower planning times compared to other graph search algorithms.The proposedmethodmaintains stable performance across maps of different scales and various port scenarios,verifying its practicality and potential for wider application.
基金Philosophy and Social Sciences Research Project of Shandong Higher Education Institutions:“Research on the Double Helix Mechanism of Yellow River Culture Empowering Ideological and Political Education in Universities from the Perspective of Cultural Confidence Cultivation”(2025ZSYB077)Youth Key Project of Shandong Humanities and Social Sciences Research Project,“Research on Integrating Yellow River Culture into the Cultivation of University Students’Cultural Confidence”Shandong Higher Education Institutions Young Innovation Team Program:“Yellow River Delta Ecological Protection and Governance Innovation Team”(2023RW036).
文摘Guided by the significant theoretical principle of the“Two Integrations”and grounded in Marxist cultural theory as its methodological basis,this paper constructs a bidirectional interpretative model linking“Yellow River Culture”with“Cultural Confidence”.It proposes an integrated“Objective-Content-Path-Support”framework.Through the synergy of three-dimensional objectives,adaptation of stratified content,innovation in four-dimensional pathways,and support from a three-dimensional guarantee system,this framework establishes a closed-loop operational mechanism of“Curriculum-Practice-Evaluation-Feedback”.The study focuses on core issues in integrating Yellow River culture into university education practices,such as content construction,methodological pathways,and institutional guarantees.It aims to provide a systematic reference for universities to fulfill their fundamental task of“fostering virtue and cultivating talent”and to serve the national strategies for ecological protection and high-quality development in the Yellow River Basin.
文摘Taking the rural low-income population of Zhejiang Province as its subject, this paper examines how to build a sustainable income-growth mechanism and identify feasible implementation paths within the context of the common prosperity strategy. The research identifies key obstacles to income expansion, including an undiversified industrial structure, insufficient human capital, and a lack of robust social protection. These call for systemic solutions featuring institutional innovation, resource consolidation, and capability enhancement. Building on Zhejiang's experience as a common prosperity demonstration zone, the article constructs an integrated framework centered on four pillars: industrial empowerment, education upgrading, social security reinforcement, and digital coordination. It further offers concrete policy proposals involving the cultivation of localized industries, vocational skill training, enhanced safety nets, and the adoption of digital tools. The study thus offers both theoretical insights and practical paradigms for tackling the challenge of raising incomes in low-income rural areas.
基金supported by the National Natural Science Foundation of China(72571094,72271076,71871079)。
文摘Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always influenced by external factors,making the original path planning solution ineffective.In this paper,the multi-depot multi-UAV path planning problem with uncertain flight time is modeled as a robust optimization model with a budget uncertainty set.Then,the robust optimization model is transformed into a mixed integer linear programming model by the strong duality theorem,which makes the problem easy to solve.To effectively solve large-scale instances,a simulated annealing algorithm with a robust feasibility check(SA-RFC)is developed.The numerical experiment shows that the SA-RFC can find high-quality solutions within a few seconds.Moreover,the effect of the task location distribution,depot counts,and variations in robustness parameters on the robust optimization solution is analyzed by using Monte Carlo experiments.The results demonstrate that the proposed robust model can effectively reduce the risk of the UAV failing to return to the depot without significantly compromising the profit.
基金supported by the National Natural Science Foundation of China(No.U2433214)。
文摘Shenzhen,a major city in southern China,has experienced rapid advancements in Unmanned Aerial Vehicle(UAV)technology,resulting in extensive logistics networks with thousands of daily flights.However,frequent disruptions due to its subtropical monsoon climate,including typhoons and gusty winds,present ongoing challenges.Despite the growing focus on operational costs and third-party risks,research on low-altitude urban wind fields remains scarce.This study addresses this gap by integrating wind field analysis into UAV path planning,introducing key innovations to the classical model.First,UAV wind resistance and turbulence constraints are analyzed,mapping high-wind-speed and turbulence-prone zones in the airspace.Second,wind dynamics are incorporated into path planning by considering airspeed and groundspeed variation,optimizing waypoint selection and flight speed adjustments to improve overall energy efficiency.Additionally,a wind-aware Theta*algorithm is proposed,leveraging wind vectors to expedite search process,while Computational Fluid Dynamics(CFD)techniques are employed to calculate wind fields.A case study of Shenzhen,examining wind patterns over the past decade,demonstrates a 6.23%improvement in groundspeed and a 7.69%reduction in energy consumption compared to wind-agnostic models.This framework advances UAV logistics by enhancing route safety and energy efficiency,contributing to more cost-effective operations.
基金supported by the Malaysia Ministry of Higher Education under Fundamental Research Grant Scheme with Project Code:FRGS/1/2024/TK07/USM/02/3.
文摘Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller(NFIDC)with a Feedback Radial Basis Function Neural Network(FRBFNN).The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1.The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.A two-stage simulation evaluation was conducted.In the first stage,the controller was tested in a simulated hospital environment under both ideal and non-ideal conditions.In the second,it was benchmarked against four established controllers-Neural Network Model Reference Adaptive(NNMRA),Z-number Fuzzy Logic(Z-FL),Adaptive Dynamic Controller(ADC),and Fuzzy Logic-PID(FL-PID)—using circular and lemniscate trajectories.Across ten runs,the proposed controller achieved the lowest tracking errors under all conditions.Under ideal conditions,it achieved average improvements of 55.24%,75.75%,and 55.20%in integral absolute error(IAE),integral squared error(ISE),and mean absolute error(MAE),respectively,with coefficient of variation(CV)reductions above 55%.Under non-ideal conditions,average improvements exceeded 64%in IAE,77%in ISE,and 66%in MAE,while maintaining CV reductions above 57%.These results confirm that the NFIDC-FRBFNN controller offers superior accuracy,robustness,and consistency for real-time path tracking in healthcare robotics.
